kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/TensorAlgebra.kt

@@ -28,7 +28,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     public operator fun TensorType.get(i: Int): TensorType
 
     //https://pytorch.org/docs/stable/generated/torch.transpose.html
-    public fun TensorType.transpose(i: Int, j: Int): TensorType
+    public fun TensorType.transpose(i: Int = -2, j: Int = -1): TensorType
 
     //https://pytorch.org/docs/stable/tensor_view.html
     public fun TensorType.view(shape: IntArray): TensorType
@@ -40,7 +40,7 @@ public interface TensorAlgebra<T, TensorType : TensorStructure<T>> {
     //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
     public fun diagonalEmbedding(
         diagonalEntries: TensorType,
-        offset: Int = 0, dim1: Int = 0, dim2: Int = 1
+        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
     ): TensorType
 
 }

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt

@@ -198,19 +198,21 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun DoubleTensor.transpose(i: Int, j: Int): DoubleTensor {
-        checkTranspose(this.dimension, i, j)
+        val ii = minusIndex(i)
+        val jj = minusIndex(j)
+        checkTranspose(this.dimension, ii, jj)
         val n = this.linearStructure.size
         val resBuffer = DoubleArray(n)
 
         val resShape = this.shape.copyOf()
-        resShape[i] = resShape[j].also { resShape[j] = resShape[i] }
+        resShape[ii] = resShape[jj].also { resShape[jj] = resShape[ii] }
 
         val resTensor = DoubleTensor(resShape, resBuffer)
 
         for (offset in 0 until n) {
             val oldMultiIndex = this.linearStructure.index(offset)
             val newMultiIndex = oldMultiIndex.copyOf()
-            newMultiIndex[i] = newMultiIndex[j].also { newMultiIndex[j] = newMultiIndex[i] }
+            newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
 
             val linearIndex = resTensor.linearStructure.offset(newMultiIndex)
             resTensor.buffer.array()[linearIndex] =
@@ -283,16 +285,18 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
     }
 
     override fun diagonalEmbedding(diagonalEntries: DoubleTensor, offset: Int, dim1: Int, dim2: Int): DoubleTensor {
+        val d1 = minusIndexFrom(diagonalEntries.linearStructure.dim + 1, dim1)
+        val d2 = minusIndexFrom(diagonalEntries.linearStructure.dim + 1, dim2)
         val n = diagonalEntries.shape.size
-        if (dim1 == dim2) {
-            throw RuntimeException("Diagonal dimensions cannot be identical $dim1, $dim2")
+        if (d1 == d2) {
+            throw RuntimeException("Diagonal dimensions cannot be identical $d1, $d2")
         }
-        if (dim1 > n || dim2 > n) {
+        if (d1 > n || d2 > n) {
             throw RuntimeException("Dimension out of range")
         }
 
-        var lessDim = dim1
-        var greaterDim = dim2
+        var lessDim = d1
+        var greaterDim = d2
         var realOffset = offset
         if (lessDim > greaterDim) {
             realOffset *= -1

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/TensorLinearStructure.kt

@@ -73,6 +73,9 @@ public class TensorLinearStructure(public val shape: IntArray)
     public val size: Int
         get() = shape.reduce(Int::times)
 
+    public val dim: Int
+        get() = shape.size
+
     public fun indices(): Sequence<IntArray> = (0 until size).asSequence().map {
         index(it)
     }

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/utils.kt

@@ -40,3 +40,13 @@ internal inline fun getRandomNormals(n: Int, seed: Long): DoubleArray {
     val u = Random(seed)
     return (0 until n).map { sqrt(-2.0 * ln(u.nextDouble())) * cos(2.0 * PI * u.nextDouble()) }.toDoubleArray()
 }
+
+internal inline fun minusIndexFrom(n: Int, i: Int) : Int = if (i >= 0) i else {
+    val ii = n + i
+    check(ii >= 0) {
+        "Out of bound index $i for tensor of dim $n"
+    }
+    ii
+}
+
+internal inline fun <T> BufferedTensor<T>.minusIndex(i: Int): Int =  minusIndexFrom(this.linearStructure.dim, i)

kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt

@@ -130,7 +130,7 @@ class TestDoubleLinearOpsTensorAlgebra {
     fun testBatchedSVD() = DoubleLinearOpsTensorAlgebra {
         val tensor = randNormal(intArrayOf(7, 5, 3), 0)
         val (tensorU, tensorS, tensorV) = tensor.svd()
-        val tensorSVD = tensorU dot (diagonalEmbedding(tensorS,0,1,2) dot tensorV)
+        val tensorSVD = tensorU dot (diagonalEmbedding(tensorS) dot tensorV)
         assertTrue(tensor.eq(tensorSVD))
     }
 
@@ -139,7 +139,7 @@ class TestDoubleLinearOpsTensorAlgebra {
         val tensor = randNormal(shape = intArrayOf(5, 2, 2), 0)
         val tensorSigma = tensor + tensor.transpose(1, 2)
         val (tensorS, tensorV) = tensorSigma.symEig()
-        val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS, 0,1,2) dot tensorV.transpose(1, 2))
+        val tensorSigmaCalc = tensorV dot (diagonalEmbedding(tensorS) dot tensorV.transpose(1, 2))
         assertTrue(tensorSigma.eq(tensorSigmaCalc))
     }
 

kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt

@@ -35,8 +35,8 @@ class TestDoubleTensorAlgebra {
     fun transpose1x2x3() = DoubleTensorAlgebra {
         val tensor = fromArray(intArrayOf(1, 2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val res01 = tensor.transpose(0, 1)
-        val res02 = tensor.transpose(0, 2)
-        val res12 = tensor.transpose(1, 2)
+        val res02 = tensor.transpose(-3, 2)
+        val res12 = tensor.transpose()
 
         assertTrue(res01.shape contentEquals intArrayOf(2, 1, 3))
         assertTrue(res02.shape contentEquals intArrayOf(3, 2, 1))